A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods
Tóm tắt
Land subsidence occurrence in the Tasuj plane is becoming more frequent and hazardous in the near future due to the water crisis. To mitigate damage caused by land subsidence events, it is necessary to determine the susceptible or prone areas. This study focuses on producing and comparing land subsidence susceptibility map (LSSM) using boosted regression tree (BRT), random forest (RF), and classification and regression tree (CART) approaches with twelve influencing variables, namely altitude, slope angle, aspect, groundwater level, groundwater level change, land cover, lithology, distance to fault, distance to stream, stream power index, topographic wetness index, and plan curvature. Moreover, by implementing the Relief-F feature selection method, the most important variables in LSSM procedure were identified. The performance of the adopted methods was assessed using the area under the receiver operating characteristics curve (AUROC) and statistical evaluation indexes. The results showed that all the employed methods performed well; in particular, the BRT model (AUROC = 0.819) yielded higher prediction accuracy than RF (AUROC = 0.798) and CART (AUROC = 0.764). Findings of this study can assist in characterizing and mitigating the related hazard of land subsidence events.
Tài liệu tham khảo
Abedi Gheshlaghi H, Feizizadeh B, Blaschke T (2019) GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. J Environ Plann Manag. https://doi.org/10.1080/09640568.2019.1594726
Aertsen W, Kint V, van Orshoven J, Özkan K, Muys B (2010) Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecol Model 221:1119–1130. https://doi.org/10.1016/j.ecolmodel.2010.01.007
Al-Halbouni D, Holohan EP, Saberi L, Alrshdan H, Sawarieh A, Closson D, Walter TR, Dahm T (2017) Sinkholes, subsidence and subrosion on the eastern shore of the Dead Sea as revealed by a close-range photogrammetric survey. Geomorphology 285:305–324. https://doi.org/10.1016/j.geomorph.2017.02.006
Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Bennett ND, Croke BFW, Guariso G, Guillaume JHA, Hamilton SH, Jakeman AJ, Marsili-Libelli S, Newham LTH, Norton JP, Perrin C, Pierce SA, Robson B, Seppelt R, Voinov AA, Fath BD, Andreassian V (2013) Characterising performance of environmental models. Environ Modell Softw 40:1–20. https://doi.org/10.1016/j.envsoft.2012.09.011
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/a:1010933404324
Calderhead AI, Therrien R, Rivera A, Martel R, Garfias J (2011) Simulating pumping-induced regional land subsidence with the use of InSAR and field data in the Toluca Valley, Mexico. Adv Water Resour 34:83–97. https://doi.org/10.1016/j.advwatres.2010.09.017
Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma J (2017) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. CATENA 151:147–160. https://doi.org/10.1016/j.catena.2016.11.032
Choi J-K, Kim K-D, Lee S, Won J-S (2010) Application of a fuzzy operator to susceptibility estimations of coal mine subsidence in Taebaek City, Korea. Environ Earth Sci 59:1009–1022. https://doi.org/10.1007/s12665-009-0093-6
De'ath G (2007) Boosted trees for ecological modeling and prediction. Ecology 88:243–251. https://doi.org/10.1890/0012-9658(2007)88[243:BTFEMA]2.0.CO;2
Ebrahimy H, Azadbakht M (2019) Downscaling MODIS land surface temperature over a heterogeneous area: an investigation of machine learning techniques, feature selection, and impacts of mixed pixels. Comput Geosci 124:93–102. https://doi.org/10.1016/j.cageo.2019.01.004
Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Feizizadeh B (2018) A novel approach of fuzzy dempster-shafer theory for spatial uncertainty analysis and accuracy assessment of object-based image classification. IEEE Geosci Remote Sens Lett 15:18–22. https://doi.org/10.1109/LGRS.2017.2763979
Feizizadeh B, Blaschke T (2013) GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Nat Hazards 65:2105–2128. https://doi.org/10.1007/s11069-012-0463-3
Feizizadeh B, Jankowski P, Blaschke T (2014) A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis. Comput Geosci 64:81–95. https://doi.org/10.1016/j.cageo.2013.11.009
Feizizadeh B, Roodposhti MS, Blaschke T, Aryal J (2017) Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping. Arab J Geosci 10:122. https://doi.org/10.1007/s12517-017-2918-z
Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Ann Stat 28:337–407. https://doi.org/10.1214/aos/1016218223
Galloway D, Jones DR, Ingebritsen SE (eds) (1999) Land subsidence in the United States. Circular 1182. U.S. Geological Survey, Reston, Virginia
Galve JP, Gutiérrez F, Remondo J, Bonachea J, Lucha P, Cendrero A (2009) Evaluating and comparing methods of sinkhole susceptibility mapping in the Ebro Valley evaporite karst (NE Spain). Geomorphology 111:160–172. https://doi.org/10.1016/j.geomorph.2009.04.017
Gao Y, Alexander EC, Barnes RJ (2005) Karst database implementation in Minnesota: analysis of sinkhole distribution. Environ Geol 47:1083–1098. https://doi.org/10.1007/s00254-005-1241-2
Ghorbanzadeh O, Feizizadeh B, Blaschke T (2018a) An interval matrix method used to optimize the decision matrix in AHP technique for land subsidence susceptibility mapping. Environ Earth Sci 77:584. https://doi.org/10.1007/s12665-018-7758-y
Ghorbanzadeh O, Feizizadeh B, Blaschke T (2018b) Multi-criteria risk evaluation by integrating an analytical network process approach into GIS-based sensitivity and uncertainty analyses. Geomat Nat Hazards Risk 9:127–151. https://doi.org/10.1080/19475705.2017.1413012
Hastie T, Tibshirani R, Friedman J (2009) Overview of supervised learning. The elements of statistical learning: data mining, inference, and prediction. Springer, New York, pp 9–41
Jenks GF, Caspall FC (1971) Error on choroplethic maps: definition, measurement, reduction. Ann Assoc Am Geogr 61:217–244. https://doi.org/10.1111/j.1467-8306.1971.tb00779.x
Kim K-D, Lee S, Oh H-J (2009) Prediction of ground subsidence in Samcheok City, Korea using artificial neural networks and GIS. Environ Geol 58:61–70. https://doi.org/10.1007/s00254-008-1492-9
Kira K, Rendell LA (1992) A practical approach to feature selection. In: Sleeman D, Edwards P (eds) machine learning proceedings 1992. Morgan Kaufmann, San Francisco, pp 249–256
Lee S, Park I, Choi JK (2012) Spatial prediction of ground subsidence susceptibility using an artificial neural network. Environ Manag 49:347–358. https://doi.org/10.1007/s00267-011-9766-5
Lee S, Kim J-C, Jung H-S, Lee MJ, Lee S (2017) Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomat Nat Hazards Risk 8:1185–1203. https://doi.org/10.1080/19475705.2017.1308971
Li Y, Gong H, Zhu L, Li X (2017) Measuring spatiotemporal features of land subsidence, groundwater drawdown, and compressible layer thickness in Beijing Plain. China Water 9:64. https://doi.org/10.3390/w9010064
Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2:18–22
Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17:145–151. https://doi.org/10.1111/j.1466-8238.2007.00358.x
Mahmoudpour M, Khamehchiyan M, Nikudel MR, Ghassemi MR (2016) Numerical simulation and prediction of regional land subsidence caused by groundwater exploitation in the southwest plain of Tehran, Iran. Eng Geol 201:6–28. https://doi.org/10.1016/j.enggeo.2015.12.004
McKenney DW, Pedlar JH (2003) Spatial models of site index based on climate and soil properties for two boreal tree species in Ontario, Canada. For Ecol Manag 175:497–507. https://doi.org/10.1016/S0378-1127(02)00186-X
Modoni G, Darini G, Spacagna RL, Saroli M, Russo G, Croce P (2013) Spatial analysis of land subsidence induced by groundwater withdrawal. Eng Geol 167:59–71. https://doi.org/10.1016/j.enggeo.2013.10.014
Moghtased-Azar K, Mirzaei A, Nankali HR, Tavakoli F (2012) Investigation of correlation of the variations in land subsidence (detected by continuous GPS measurements) and methodological data in the surrounding areas of Lake Urmia. Nonlinear Process Geophys 19:675–683. https://doi.org/10.5194/npg-19-675-2012
Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30. https://doi.org/10.1002/hyp.3360050103
Motagh M, Shamshiri R, Haghshenas Haghighi M, Wetzel H-U, Akbari B, Nahavandchi H, Roessner S, Arabi S (2017) Quantifying groundwater exploitation induced subsidence in the Rafsanjan plain, southeastern Iran, using InSAR time-series and in situ measurements. Eng Geol 218:134–151. https://doi.org/10.1016/j.enggeo.2017.01.011
Naghibi SA, Pourghasemi HR, Dixon B (2015) GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess 188:44. https://doi.org/10.1007/s10661-015-5049-6
Oh H-J, Lee S (2010) Assessment of ground subsidence using GIS and the weights-of-evidence model. Eng Geol 115:36–48. https://doi.org/10.1016/j.enggeo.2010.06.015
Park I, Lee J, Saro L (2014) Ensemble of ground subsidence hazard maps using fuzzy logic. Cent Eur J Geosci 6:207–218. https://doi.org/10.2478/s13533-012-0175-y
Perrin J, Cartannaz C, Noury G, Vanoudheusden E (2015) A multicriteria approach to karst subsidence hazard mapping supported by weights-of-evidence analysis. Eng Geol 197:296–305. https://doi.org/10.1016/j.enggeo.2015.09.001
Peterson AT, Papeş M, Soberón J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Model 213:63–72. https://doi.org/10.1016/j.ecolmodel.2007.11.008
Pham BT, Tien Bui D, Prakash I, Dholakia MB (2017) Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149:52–63. https://doi.org/10.1016/j.catena.2016.09.007
Pradhan B, Abokharima MH, Jebur MN, Tehrany MS (2014) Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73:1019–1042. https://doi.org/10.1007/s11069-014-1128-1
Prasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–199. https://doi.org/10.1007/s10021-005-0054-1
R Core Team (2017) R: A language and Environment for Statistical Computing. R Foundation for Statistical Computing, Austria, Vienna.
Robnik-Šikonja M, Kononenko I (2003) Theoretical and empirical analysis of relief and relief. Mach Learn 53:23–69. https://doi.org/10.1023/a:1025667309714
Rodrigues M, de la Riva J (2014) An insight into machine-learning algorithms to model human-caused wildfire occurrence. Environm Modell Softw 57:192–201. https://doi.org/10.1016/j.envsoft.2014.03.003
Rosi A, Tofani V, Agostini A, Tanteri L, Tacconi Stefanelli C, Catani F, Casagli N (2016) Subsidence mapping at regional scale using persistent scatters interferometry (PSI): the case of Tuscany region (Italy). Int J Appl Earth Obs Geoinf 52:328–337. https://doi.org/10.1016/j.jag.2016.07.003
Sadeghi Z, Zoej MJV, Dehghani M (2013) An improved persistent scatterer interferometry for subsidence monitoring in the Tehran Basin. IEEE J Sel Top Appl Earth Observ Remote Sens 6:1571–1577. https://doi.org/10.1109/JSTARS.2013.2259221
Sahu SP, Yadav M, Das AJ, Prakash A, Kumar A (2017) Multivariate statistical approach for assessment of subsidence in Jharia coalfields. India Arab J Geosci 10:191. https://doi.org/10.1007/s12517-017-2985-1
Santos SMd, Cabral JJdSP, Pontes Filho IDdS (2012) Monitoring of soil subsidence in urban and coastal areas due to groundwater overexploitation using GPS. Nat Hazards 64:421–439. https://doi.org/10.1007/s11069-012-0247-9
Sayyaf M, Mahdavi M, Barani OR, Feiznia S, Motamedvaziri B (2014) Simulation of land subsidence using finite element method: rafsanjan plain case study. Nat Hazards 72:309–322. https://doi.org/10.1007/s11069-013-1010-6
Schapire RE (2003) The boosting approach to machine learning: an overview. In: Denison DD, Hansen MH, Holmes CC, Mallick B, Yu B (eds) Nonlinear estimation and classification. Springer, New York, pp 149–171
Shataeea S, Weinaker H, Babanejad M (2011) Plot-level forest volume estimation using airborne laser scanner and TM data, comparison of boosting and random forest tree regression algorithms. Procedia Environ Sci 7:68–73. https://doi.org/10.1016/j.proenv.2011.07.013
Shi X, Wu J, Ye S, Zhang Y, Xue Y, Wei Z, Li Q, Yu J (2008) Regional land subsidence simulation in Su-Xi-Chang area and Shanghai City, China. Eng Geol 100:27–42. https://doi.org/10.1016/j.enggeo.2008.02.011
Shviro M, Haviv I, Baer G (2017) High-resolution InSAR constraints on flood-related subsidence and evaporite dissolution along the Dead Sea shores: Interplay between hydrology and rheology. Geomorphology 293:53–68. https://doi.org/10.1016/j.geomorph.2017.04.033
Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293
Zhou G, Yan H, Chen K, Zhang R (2016) Spatial analysis for susceptibility of second-time karst sinkholes: a case study of Jili Village in Guangxi, China. Comput Geosci 89:144–160. https://doi.org/10.1016/j.cageo.2016.02.001
Zhu L, Gong H, Li X, Wang R, Chen B, Dai Z, Teatini P (2015) Land subsidence due to groundwater withdrawal in the northern Beijing plain, China. Eng Geol 193:243–255. https://doi.org/10.1016/j.enggeo.2015.04.020